cv-detection

name	cv-detection
description	Best practices for object detection tasks. Use when working on COCO, VOC, or detection architectures like YOLO and DETR.
metadata	{"category":"domain","trigger-keywords":"detection,object,bbox,yolo,coco,anchor,faster rcnn","applicable-stages":"9,10","priority":"5","version":"1.0","author":"researchclaw","references":"Ren et al., Faster R-CNN, NeurIPS 2015; Carion et al., End-to-End Object Detection with Transformers, ECCV 2020"}

Object Detection Best Practice

Architecture families:

• One-stage: YOLO (v5/v8), SSD, RetinaNet, FCOS
• Two-stage: Faster R-CNN, Cascade R-CNN
• Transformer: DETR, DINO, RT-DETR

Training recipe:

• Use pre-trained backbone (ImageNet)
• Multi-scale training and testing
• IoU threshold: 0.5 for mAP50, 0.5:0.95 for mAP
• Use FPN for multi-scale feature extraction
• Focal loss for class imbalance in one-stage detectors

Standard benchmarks:

• COCO val2017: ~37 mAP (Faster R-CNN R50), ~51 mAP (DINO Swin-L)
• Pascal VOC: ~80 mAP50 (Faster R-CNN)